Business enterprises, such as manufacturing facilities and companies, can be confronted with the challenge of maximizing service to their customers while minimizing the cost of operations. One primary component of these costs, and a significant factor influencing the level of service delivered to customers, can be the location and amount of inventory stored in a company's supply chain network. In general, higher inventory levels can offer both higher costs and higher levels of service. Conventional inventory management systems may not provide effective mechanisms for optimally configuring an existing supply chain network that include multiple echelons of interacting supply locations subject to uncertainty over the amount and timing of demand for finished products, and subject to uncertainty over the timing and reliability of resupply activities. In order to obtain a solution, various methods of analysis used in conventional inventory management systems can make significant simplifying assumptions, which may limit the applicability of such methods to relatively simple systems or otherwise risking an inaccurate description of the expected performance of the supply chain system.
As disclosed in U.S. patent application Ser. No. 10/685,670, entitled “SYSTEMS AND METHODS FOR IMPROVING PLANNING, SCHEDULING, AND SUPPLY CHAIN MANAGEMENT,” a flow path management system (FPMS) or a supply chain management system (SCMS) can include a lot sizer and work in progress (WIP) estimator (LWE) module and an inventory advisor (IA) module. Such systems and modules can be implemented by a computer system, such as 100 in FIG. 1 of the above referenced patent application.
The LWE module can implement methods and/or algorithms to accomplish some or all of the following: (a) identify constrained resources, (b) optimize how many products should be produced together to minimize setups while meeting output goals, and (c) optimize WIP levels to reach throughput and cycle time objectives. Some conventional methods and/or algorithms for inventory management systems can monitor and control the WIP levels in a single loop or subset of items or products in a system or flow path. However, such methods and/or algorithms were limited to applying WIP levels to subsets of items or products in a single flow path, and determining an optimal WIP level for the system aggregated over the various items or products.
The inventory advisor (IA) module can implement methods and/or algorithms to accomplish some or all of the following: (a) determine stocking policies, such as which products should be made to order and which should be made to stock; (b) set reorder policies, such as determining reorder points and reorder quantities for make to stock products; and (c) generate an exchange curve to describe a best cost that can be obtained for a level of performance associated with a particular flow path, system, or set of items or products, such as displaying a policy cost for a single product or for multiple products as a function of order frequency and/or service levels. However, at least some of mathematical equations used by the IA module to analyze inventory policies assume that policy parameters that are manipulated to create the measured performance (i.e. order frequency, and service level) are continuous and unbounded. Furthermore, in some instances when the IA module determined an aggregate performance for a group of products with corresponding exchange curves, there were no assumed dependencies between any of the products.
In addition, the IA module can utilize user input data to describe demand and lead times for a flow path using commonly known statistical techniques such as average and standard deviation. In many instances, a Poisson or normal distribution can be applied to the user input data, and a representation of the flow path performance can be generated by the IA module, however in some instances, the representation of the flow path performance based on such distributions may not be indicative of or otherwise an accurate representation of flow path performance. Therefore, a need for improved systems and methods for an inventory advisor (IA) module exists.
Exchange curves can be generated by an IA module using equally spaced intervals of an intermediate parameter called β. This variable can be a surrogate for the backorder cost, a parameter which is rarely known by the user. In many instances, displays show the relationship between inventory cost and service level, but the service level varies non-linearly with β. The result can be that in the points of interest along the exchange curve, and those most likely to be selected by a user interested in such points, such points may be highly concentrated at the high end of the service distribution (near 100%). Therefore, a need for improved systems and methods for generating exchange curves exists.
Approximate equations can be utilized by an IA module to determine an “optimal” reorder point for a flow path. While such equations can be accurate under certain circumstances, in some instances relatively large inaccuracies can be induced when the reorder quantity is low or if the targeted service level is low. Therefore, a need for improved systems and methods to determine a reorder point which describes the probability of a stockout exists.
Conventional inventory management systems can utilize separate methods for determining optimal lot sizes and for determining optimal inventory stocking levels. However, potential interaction between decisions made for optimizing lot sizes and optimizing inventory stocking levels is typically unaccounted for. Such interaction may be particularly evident when a setup carries an identifiable monetary cost, for instance the production of scrap; when a selected lot size has a significant impact on a lead time for a material through a production facility; or when holding costs of products being analyzed suggest that a high cost of some products warrants special treatment (i.e. shorter lead times). The interaction may occur in such cases because lot sizes selected by the LWE module for a particular product may have a direct impact on order quantity and replenishment lead times that affect or otherwise drive selection of a reorder point by an inventory advisor (IA) module.
As disclosed in U.S. patent application Ser. No. 10/685,670, systems and methods for analyzing inventory in a flow path, also known as “standard single echelon inventory analysis,” can rely upon user data input, such as descriptions of statistical distributions for lead time and demand data, as input for a problem solution. Reorder points and reorder quantities can then be determined based in part on the user data input. However, such analysis can provide little or no support for a user to determine what lead time distribution should be given the expected state and demand on an associated manufacturing system. Furthermore, the reorder quantities may not be aligned with constraints of the manufacturing system. Therefore, a need exists for improved systems and methods for analyzing inventory in a flow path.
Various topologies, schematics, and representations have been used to model a flow path or supply chain. In some instances, the topology of a supply chain can impact techniques and/or algorithms available to analyze the supply chain, with relatively more complicated topologies causing more significant and restrictive assumptions in a supply chain analysis. Examples of conventional topologies used to describe and model supply chains are shown in FIG. 16. As conventional theories may focuses on analyzing “pure” assembly, “pure” distribution, or “pure” serial networks, a challenge in solving supply chain problems is producing one or more solutions for supply chain with mixed topologies (i.e. having attributes of more than one of the topologies depicted in FIG. 16). Therefore, a need exists for systems and methods for performing multi-echelon inventory analysis and for optimizing a multi-echelon inventory system.
In conventional inventory management systems, partitioning planning information is sometimes limited to predetermined assumptions which can be used to partition entities into different logical sets. For example, products in an inventory can be partitioned into logical groups such as flow paths. Flow paths can include one or more route steps. Each route step in a flow path can be partitioned into logical groups such as control loops. These flow paths, route steps, control loops, and other logical groups are oftentimes predetermined and assumed partitions used by a conventional inventory management system to perform various performance analyses. However, conventional systems and methods for partitioning planning information can be somewhat inflexible since partitions are assumed, or can be ad hoc, or may otherwise be limited due to applied assumptions. Therefore, a need exists for systems and methods for partitioning planning information.